نشریه پیشرفت های مالی و سرمایه گذاری
پیاپی 13 (زمستان 1402)

The purpose of this research was to examine the role of the mutual information content of earnings from market indices in the synchrony of returns and portfolio performance.

This study was applied, descriptive, and analytical. The statistical population consisted of all companies listed on the Tehran Stock Exchange during the years 2012 to 2021. After screening, 118 companies were selected as the statistical sample. Joint entropy was used to measure the mutual information content of the variables. The Rahavard Novin software and the website of the Securities and Exchange Organization were used for data collection. Student's t-tests, mean comparison, paired tests, Levene's test for equality of variances, and bootstrap in hypothesis testing were employed, along with the R software for analysis.

The results indicated that while the mutual information of company earnings from market indices leads to greater synchrony in returns, it is not statistically significant. Additionally, the results showed that portfolios with a coefficient of mutual information from earnings perform differently than traditional portfolios. The findings of the study suggest that portfolios formed from the top 20% coefficient of mutual information, overall and in holding periods of 6, 9, and 12 months, performed better than traditional and other portfolios.

Originality / Value:

 The contribution of this research includes the use of joint entropy as a tool to measure the mutual information content of earnings from the market and examining its effect on synchrony, as well as presenting a new model for portfolio formation.

With the expansion of globalization, financial markets in both developed and developing countries have become interconnected. In financial discourse, the interconnection between financial markets is referred to as financial contagion. Financial contagion can transmit the volatility of one market to another, potentially leading to economic booms, recessions, or varying levels of risk and return. Consequently, given the significance of volatility contagion in markets for investors and decision-makers, the purpose of this study is to examine the effect of dynamic contagion in the volatility cycle between the futures market of gold and the foreign exchange rate in financial markets and the Tehran Stock Exchange.

The data were collected daily over the period from 2009 to 2018. To test the research hypotheses, GARCH-BEKK models, Markov Switching, and Vector Autoregression were employed.

The results of the study indicate that the contagion effect of volatility is from the foreign exchange market to the gold futures market. Moreover, the contagion effect of volatility from the foreign exchange market to the gold futures market varies across different regimes.

Originality / Value: 

The findings of this research could provide new insights that may guide policymaking and decision-making in the financial industry.

The goal is to select an optimal portfolio of stocks by allocating capital among various investment opportunities in the stock market to achieve maximum return at a specified level of risk. This constitutes an efficient portfolio.

Attaining an efficient portfolio involves solving an optimization problem. There are numerous techniques and tools available to solve this issue. In this study, 15 stocks from companies listed on the Tehran Stock Exchange, including symbols such as Khapars, Khazamiya, Vepasar, Foulad, Akhabar, Kegel, Femli, Tapiko, Sepaha, Fazer, Fakhas, Shohbaran, Shefan, Qamro and Qathabat, were selected using cluster sampling. First, the daily returns of these stocks were calculated over a 5-year period from 2015 to 2020 (1183 days). The risk of the optimal investment portfolio was then calculated using the Mean-Semi Variance and Conditional Value at Risk models. These two criteria were compared using a classic solution method. Subsequently, the output data obtained from these calculations were compared using MATLAB software, employing the Particle Swarm Optimization algorithm under the Mean-Semi Variance risk criterion and the Genetic Algorithm under the Conditional Value at Risk criterion.

The results of this study indicate that the meta-heuristic Particle Swarm Optimization method yields a higher portfolio return ratio compared to the Genetic Algorithm in the Mean-Semi Variance risk criterion.

Originality / Value: 

This research utilizes multi-objective genetic algorithms and Particle Swarm Optimization, which are intelligent and novel algorithms, to minimize the objective function value using Conditional Value at Risk and Mean-Semi Variance criteria. These algorithms optimize the return and risk ratios of the stocks in the investment portfolio with the highest possible accuracy. Additionally, the efficiency comparison of these models using MATLAB software contributes an innovative aspect to this study.

Procedural justice refers to employees' perceptions of fairness in the processes adopted within organizational procedures. Interactional justice pertains to employees' perceptions of fairness in interpersonal or procedural aspects, divided into two categories: informational and interpersonal. Informational justice refers to the explanations provided to employees. Interpersonal justice relates to the degree of goodwill, dignity, and respect with which superiors interact. The use of multiple performance scales influences organizational commitment as well as distributive and procedural justice. In other words, managers believe that the use of multiple scales increases the likelihood of more accurate performance assessments. Consequently, the aim of this research is to investigate the impact of behavioral accounting components on the development of perceptions of justice and organizational commitment.

The target population of this research comprised accountants in the private and public sectors, selected through random sampling and examined over a six-month period. The research tool was standardized questionnaires, and Partial Least Squares (PLS) analysis was employed for fitting and testing research hypotheses.

The results of this study demonstrate that the impact of behavioral accounting characteristics on social responsibility and the principle of controllability has a significant positive effect. Decision-making is a managerial duty and the core of management. Managerial decision-making indicates the necessary work and supports accounting information in a specific organization. Performance issues are a classic problem in companies and should be addressed by economists to determine performance.

Originality / Value:

 The integration of behavioral sciences into accounting significantly enhances the profession's growth and brings new insights for accountants. Awareness of the relationship between individual behavior and accounting equips accountants with new tools for evaluating and solving problems in business units. This research, for the first time, examines the impact of behavioral accounting components on the development of justice perception and organizational commitment.

The current research aims to systematically examine bankruptcy prediction models with the goal of developing a model that serves as a guide for selecting the most suitable tools. These tools should ideally align with the existing data and quality criteria of bankruptcy prediction models.

To conduct this research, a systematic search was performed on the Web of Science database using keywords such as Bankruptcy, Default, Distress, Failure, Forecasting, Predicting, Prediction, and Insolvency, spanning the years 2015 to 2023. Based on defined inclusion and exclusion criteria, this search yielded 1000 articles, out of which 49 were ultimately selected and analyzed. The findings from these articles were then summarized in tables. Subsequently, major bankruptcy prediction models were compared based on nine key criteria, and final conclusions were drawn.

Artificial neural networks and support vector machines were found to have the highest accuracy, while multiple personality analysis showed the lowest accuracy. Additionally, artificial neural networks, multiple personality analysis, decision trees, and logistic regression require a large training sample to logically identify and precisely classify patterns. However, case-based reasoning, rough sets, and support vector machines can work with smaller sample sizes.

Originality/ Value:

 The outcomes of this research contribute to a comprehensive understanding of the characteristics of tools used in developing bankruptcy prediction models and the shortcomings associated with them.

The capability of intelligent systems in predicting economic and financial variables, particularly stock prices, has been confirmed in previous research in Iran and other countries. However, the valuation of block transactions is calculated for the first time in this study. The aim is to investigate the outcomes and information from the financial reports of listed companies on the Tehran Stock Exchange using 15 financial indices and determine the impact of these indices on the valuation of block transactions by employing the RMSE test on the Test dataset.

For this purpose, financial information from 64 companies within the accepted companies of the Tehran Stock Exchange for the period from 1390 to 1400 has been utilized. The research hypothesis is tested using the Long Short-Term Memory (LSTM) deep learning neural network model.

The LSTM neural network, due to its high capability in training data, appropriate weights for these data, and creating a path that efficiently and accurately produces acceptable results for predicting the valuation of block transactions.

Originality/Value: 

In the proposed model, by measuring the valuation of block transactions, we will scrutinize the prices of these transactions and the effects of information and liquidity in large-sized transactions.

The purpose of the current research is to create an optimal portfolio using machine learning algorithms and fuzzy theory, which has a better return than the average return of the market (total index of the stock exchange).

In this article, the stocks of the selected companies are classified in the first stage using the two introduced algorithms. In the next step, stocks that entered the positive class are predicted for the next trading day with the help of random forest algorithm. For each company, three predictions are made, which are the inputs of fuzzy method optimization. Optimization is done with the aim of minimizing the risk with risk measures of value at risk and value at conditional risk. Shares information is five years old (daily) and its time period is from the beginning of 2017 to the end of 2021.

In the end, each of the algorithms and the risk measure used were measured and compared with the actual market return. Based on the obtained results, the CVAR risk measure has a better capability and result than the VAR risk measure, and the support vector machine algorithm has also achieved a better performance in choosing the investment portfolio.

Originality/ value: 

This research is optimized in the form of a capital sample by integrating machine learning methods and risk measures. Adding VaR and CVaR risk metrics enhances the decision-making process regarding risk reduction. Forecasting with the help of random forest and using an approach based on fuzzy theory for risk and value analysis gives the research an innovative perspective in portfolio formation. The findings provide investors and researchers with valuable insights in their search for better investment strategies.